An artificial intelligence audit gathers evidence about how a system produced a result. Reviewers may inspect data preparation, model versions, access controls, decision records, infrastructure and the reproducibility of earlier outputs.
High-stakes audits need durable evidence because a decision may be challenged years later. The process should preserve provenance, state and control history while protecting sensitive information and stating the limits of what the audit can establish.
Acronyms and aliases
AI audit acronymartificial intelligence audit variant
Related terms
Frequently asked questions
What evidence does an artificial intelligence audit use?
It can use data lineage, model and software versions, execution records, access controls, validation results and reproduced outcomes.
Why must artificial intelligence results be reproducible?
Reproducibility lets investigators verify the conditions and evidence that produced an earlier result rather than trusting a current explanation.
